Introduction: LIH Department TMOH

Here are preliminary results of the bibliometric mapping of the 2022 Luxembourg research evaluation. Its purpose is:

The method for the research-field-mapping can be reviewed here:

Rakas, M., & Hain, D. S. (2019). The state of innovation system research: What happens beneath the surface?. Research Policy, 48(9), 103787.

Seed Articles

The seed articles deemed representative for the active areas of research in the institution, and include authors affiliated with the institution. They can be selected in three ways:

  1. Via bibliographic clustering of the institutions publications and selection of most central articles per cluster (only clsuters where n >= 0.05N). Selection can be found at: https://github.com/daniel-hain/biblio_lux_2022/blob/master/output/seed/scopus_lih_tmoh_seed.csv
  2. Manual selection of relevant publications.
  3. A combination of 1. and 2.

The present analysis is based on the following seed articles:

AU PY TI JI
GONDER S;FERNANDEZ BOTANA I… 2020 METHOD FOR THE ANALYSIS OF THE TUMOR MICROENVIRONMENT BY MASS CYTOMETRY: APPLICATION TO CHRONIC L… FRONT. IMMUNOL.
DITTMAR G;WINKLHOFER KF 2020 LINEAR UBIQUITIN CHAINS: CELLULAR FUNCTIONS AND STRATEGIES FOR DETECTION AND QUANTIFICATION FRONT. CHEM.
SCHLICKER L;BOERS HM;DUDEK … 2019 POSTPRANDIALMETABOLIC EFFECTS OF FIBERMIXES REVEALED BY IN VIVO STABLE ISOTOPE LABELING IN HUMANS METABOLITES
DUFRESNE J;BOWDEN P;THAVARA… 2018 THE PLASMA PEPTIDOME 03 CHEMICAL SCIENCES 0301 ANALYTICAL CHEMISTRY CLIN. PROTEOMICS
HIPP G;VAILLANT M;DIEDERICH… 2018 THE LUXEMBOURG PARKINSON’S STUDY: A COMPREHENSIVE APPROACH FOR STRATIFICATION AND EARLY DIAGNOSIS FRONT. AGING NEUROSCI.
BAHLAWANE C;SCHMITZ M;LETEL… 2017 INSIGHTS INTO LIGAND STIMULATION EFFECTS ON GASTRO-INTESTINAL STROMAL TUMORS SIGNALLING CELL. SIGNAL.

Topic modelling

Here, we report the results of a LDA topic-modelling (basically, clustering on words) on all title+abstract texts.

Topics by topwords

Note: While this static vies is helpful, I recommend using the interactive LDAVis version to be found under https://daniel-hain.github.io/biblio_lux_2022/output/topic_modelling/LDAviz_lih_tmoh.rds/index.html#topic=1&lambda=0.60&term=. For functionality and usage, see technical description in the next tab.

Topics over time

Technical Description

LDA Topic Modelling

Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA, Blei et al., 2003) is an example of topic model and is used to classify text in a document to a particular topic.

LDA is a generative probabilistic model that assumes each topic is a mixture over an underlying set of words, and each document is a mixture of over a set of topic probabilities. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.

LDAVis

LDAvis is a web-based interactive visualisation of topics estimated using LDA (Sievert & Shirley, 2014). It provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization. The visualisation has two basic pieces.

The left panel visualise the topics as circles in the two-dimensional plane whose centres are determined by computing the Jensen–Shannon divergence between topics, and then by using multidimensional scaling to project the inter-topic distances onto two dimensions. Each topic’s overall prevalence is encoded using the areas of the circles.

The right panel depicts a horizontal bar chart whose bars represent the individual terms that are the most useful for interpreting the currently selected topic on the left. A pair of overlaid bars represent both the corpus-wide frequency of a given term as well as the topic-specific frequency of the term.

The \(\lambda\) slider allows to rank the terms according to term relevance. By default, the terms of a topic are ranked in decreasing order according their topic-specific probability ( \(\lambda\) = 1 ). Moving the slider allows to adjust the rank of terms based on much discriminatory (or “relevant”) are for the specific topic. The suggested optimal value of \(\lambda\) is 0.6.

Knowledge Bases: Co-Citation network analysis

Note: This analysis refers the co-citation analysis, where the cited references and not the original publications are the unit of analysis. See tab Technical descriptionfor additional explanations

Knowledge Bases summary

In order to partition networks into components or clusters, we deploy a community detection technique based on the Lovain Algorithm (Blondel et al., 2008). The Lovain Algorithm is a heuristic method that attempts to optimize the modularity of communities within a network by maximizing within- and minimizing between-community connectivity. We identify the following communities = knowledge bases.

name dgr_int dgr
Knowledge Base 1: KB 1: unlabeled (n = 2065, density =3.11)
KOMANDER D. RAPE M. THE UBIQUITIN CODE (2012) 7134 8794
SWATEK K.N. KOMANDER D. UBIQUITIN MODIFICATIONS (2016) 3971 4552
YAU R. RAPE M. THE INCREASING COMPLEXITY OF THE UBIQUITIN CODE (2016) 3682 3974
HERSHKO A. CIECHANOVER A. THE UBIQUITIN SYSTEM (1998) 3557 4494
MEYER H.J. RAPE M. ENHANCED PROTEIN DEGRADATION BY BRANCHED UBIQUITIN CHAINS (2014) 2449 2517
HUSNJAK K. DIKIC I. UBIQUITIN-BINDING PROTEINS: DECODERS OF UBIQUITIN-MEDIATED CELLULAR FUNCTIONS (2012) 2269 2641
MEVISSEN T.E.T. KOMANDER D. MECHANISMS OF DEUBIQUITINASE SPECIFICITY AND REGULATION (2017) 2154 2487
YE Y. RAPE M. BUILDING UBIQUITIN CHAINS: E2 ENZYMES AT WORK (2009) 1305 1347
DESHAIES R.J. JOAZEIRO C.A. RING DOMAIN E3 UBIQUITIN LIGASES (2009) 1275 1410
WENZEL D.M. LISSOUNOV A. BRZOVIC P.S. KLEVIT R.E. UBCH7 REACTIVITY PROFILE REVEALS PARKIN AND HHARI TO BE RING/HECT HYBRIDS (2011) 1236 1584
Knowledge Base 2: KB 2: unlabeled (n = 1308, density =2.52)
HUGHES A.J. DANIEL S.E. KILFORD L. LEES A.J. ACCURACY OF CLINICAL DIAGNOSIS OF IDIOPATHIC PARKINSON’S DISEASE: A CLINICO-PATHOLOGICAL STUDY OF 100 … 2178 2178
GILMAN S. WENNING G.K. LOW P.A. SECOND CONSENSUS STATEMENT ON THE DIAGNOSIS OF MULTIPLE SYSTEM ATROPHY (2008) 1151 1151
POSTUMA R.B. BERG D. STERN M. MDS CLINICAL DIAGNOSTIC CRITERIA FOR PARKINSON’S DISEASE (2015) 963 963
LITVAN I. AGID Y. CALNE D. CLINICAL RESEARCH CRITERIA FOR THE DIAGNOSIS OF PROGRESSIVE SUPRANUCLEAR PALSY (STEELE-RICHARDSON-OLSZEWSKI SYNDROME) 930 930
HOEHN M.M. YAHR M.D. PARKINSONISM: ONSET PROGRESSION AND MORTALITY (1967) 891 891
TOMLINSON C.L. STOWE R. PATEL S. RICK C. GRAY R. CLARKE C.E. SYSTEMATIC REVIEW OF LEVODOPA DOSE EQUIVALENCY REPORTING IN PARKINSON’S DISEASE (2010) 777 777
BERG D. POSTUMA R.B. ADLER C.H. MDS RESEARCH CRITERIA FOR PRODROMAL PARKINSON’S DISEASE (2015) 656 656
GOLBE L.I. OHMAN-STRICKLAND P.A. A CLINICAL RATING SCALE FOR PROGRESSIVE SUPRANUCLEAR PALSY (2007) 642 642
EMRE M. AARSLAND D. BROWN R. CLINICAL DIAGNOSTIC CRITERIA FOR DEMENTIA ASSOCIATED WITH PARKINSON’S DISEASE (2007) 539 539
HELY M.A. REID W.G. ADENA M.A. HALLIDAY G.M. MORRIS J.G. THE SYDNEY MULTICENTER STUDY OF PARKINSON’S DISEASE: THE INEVITABILITY OF DEMENTIA AT 20 Y… 435 435
Knowledge Base 3: KB 3: unlabeled (n = 921, density =6.27)
BRUGGNER R.V. BODENMILLER B. DILL D.L. TIBSHIRANI R.J. NOLAN G.P. AUTOMATED IDENTIFICATION OF STRATIFYING SIGNATURES IN CELLULAR SUBPOPULATIONS (2014) 1491 1491
VAN DER MAATEN L. HINTON G. VISUALIZING DATA USING T-SNE (2008) 1155 1158
WEBER L.M. ROBINSON M.D. COMPARISON OF CLUSTERING METHODS FOR HIGH-DIMENSIONAL SINGLE-CELL FLOW AND MASS CYTOMETRY DATA (2016) 1093 1093
SPITZER M.H. NOLAN G.P. MASS CYTOMETRY: SINGLE CELLS MANY FEATURES (2016) 1026 1026
LEVINE J.H. DATA-DRIVEN PHENOTYPIC DISSECTION OF AML REVEALS PROGENITOR-LIKE CELLS THAT CORRELATE WITH PROGNOSIS (2015) 948 948
SAMUSIK N. GOOD Z. SPITZER M.H. DAVIS K.L. NOLAN G.P. AUTOMATED MAPPING OF PHENOTYPE SPACE WITH SINGLE-CELL DATA (2016) 888 888
BENDALL S.C. NOLAN G.P. ROEDERER M. CHATTOPADHYAY P.K. A DEEP PROFILER’S GUIDE TO CYTOMETRY (2012) 762 762
BANDURA D.R. BARANOV V.I. ORNATSKY O.I. ANTONOV A. KINACH R. LOU X. PAVLOV S. TANNER S.D. MASS CYTOMETRY: TECHNIQUE FOR REAL TIME SINGLE CELL MULTI… 748 748
SHEKHAR K. BRODIN P. DAVIS M.M. CHAKRABORTY A.K. AUTOMATIC CLASSIFICATION OF CELLULAR EXPRESSION BY NONLINEAR STOCHASTIC EMBEDDING (ACCENSE) 698 698
KOTECHA N. KRUTZIK P.O. IRISH J.M. WEB-BASED ANALYSIS AND PUBLICATION OF FLOW CYTOMETRY EXPERIMENTS (2010) 679 679
Knowledge Base 4: KB 4: unlabeled (n = 728, density =12.89)
MICHEAU O. TSCHOPP J. INDUCTION OF TNF RECEPTOR I-MEDIATED APOPTOSIS VIA TWO SEQUENTIAL SIGNALING COMPLEXES (2003) 1353 2007
HAAS T.L. EMMERICH C.H. GERLACH B. SCHMUKLE A.C. CORDIER S.M. RIESER E. FELTHAM R. WENGER T. RECRUITMENT OF THE LINEAR UBIQUITIN CHAIN ASSEMBLY COM… 1251 1384
KIRISAKO T. KAMEI K. MURATA S. KATO M. FUKUMOTO H. KANIE M. SANO S. IWAI K. A UBIQUITIN LIGASE COMPLEX ASSEMBLES LINEAR POLYUBIQUITIN CHAINS (2006) 1212 1738
DRABER P. KUPKA S. REICHERT M. DRABEROVA H. LAFONT E. DE MIGUEL D. SPILGIES L. HARTWIG T. LUBAC-RECRUITED CYLD AND A20 REGULATE GENE ACTIVATION AND… 1055 1130
GERLACH B. CORDIER S.M. SCHMUKLE A.C. EMMERICH C.H. RIESER E. HAAS T.L. WEBB A.I. WONG W.W. LINEAR UBIQUITINATION PREVENTS INFLAMMATION AND REGULAT… 990 1090
BERTRAND M.J. MILUTINOVIC S. DICKSON K.M. HO W.C. BOUDREAULT A. DURKIN J. GILLARD J.W. BARKER P.A. CIAP1 AND CIAP2 FACILITATE CANCER CELL SURVIVAL … 972 999
HE S. WANG L. MIAO L. WANG T. DU F. ZHAO L. WANG X. RECEPTOR INTERACTING PROTEIN KINASE-3 DETERMINES CELLULAR NECROTIC RESPONSE TO TNF-ALPHA (2009) 972 989
OBERST A. DILLON C.P. WEINLICH R. MCCORMICK L.L. FITZGERALD P. POP C. HAKEM R. GREEN D.R. CATALYTIC ACTIVITY OF THE CASPASE-8-FLIP(L) 819 834
PELTZER N. RIESER E. TARABORRELLI L. DRABER P. DARDING M. PERNAUTE B. SHIMIZU Y. MONTINARO A. HOIP DEFICIENCY CAUSES EMBRYONIC LETHALITY BY ABERRAN… 751 786
WANG L. DU F. WANG X. TNF-ALPHA INDUCES TWO DISTINCT CASPASE-8 ACTIVATION PATHWAYS (2008) 721 1069
Knowledge Base 5: KB 5: unlabeled (n = 666, density =12.62)
MIETTINEN M. LASOTA J. GASTROINTESTINAL STROMAL TUMORS: PATHOLOGY AND PROGNOSIS AT DIFFERENT SITES (2006) 1351 1351
HIROTA S. ISOZAKI K. MORIYAMA Y. GAIN-OF-FUNCTION MUTATIONS OF C-KIT IN HUMAN GASTROINTESTINAL STROMAL TUMORS (1998) 1285 1285
VERWEIJ J. CASALI P.G. ZALCBERG J. PROGRESSION-FREE SURVIVAL IN GASTROINTESTINAL STROMAL TUMOURS WITH HIGH-DOSE IMATINIB: RANDOMISED TRIAL (2004) 1116 1116
HEINRICH M.C. CORLESS C.L. DUENSING A. PDGFRA ACTIVATING MUTATIONS IN GASTROINTESTINAL STROMAL TUMORS (2003) 1070 1070
DEMETRI G.D. VON MEHREN M. BLANKE C.D. EFFICACY AND SAFETY OF IMATINIB MESYLATE IN ADVANCED GASTROINTESTINAL STROMAL TUMORS (2002) 1053 1053
DEMETRI G.D. VAN OOSTEROM A.T. GARRETT C.R. EFFICACY AND SAFETY OF SUNITINIB IN PATIENTS WITH ADVANCED GASTROINTESTINAL STROMAL TUMOUR AFTER FAILUR… 907 907
BLANKE C.D. RANKIN C. DEMETRI G.D. PHASE III RANDOMIZED INTERGROUP TRIAL ASSESSING IMATINIB MESYLATE AT TWO DOSE LEVELS IN PATIENTS WITH UNRESECTAB… 806 806
HEINRICH M.C. CORLESS C.L. DEMETRI G.D. KINASE MUTATIONS AND IMATINIB RESPONSE IN PATIENTS WITH METASTATIC GASTROINTESTINAL STROMAL TUMOR (2003) 739 739
CORLESS C.L. BARNETT C.M. HEINRICH M.C. GASTROINTESTINAL STROMAL TUMOURS: ORIGIN AND MOLECULAR ONCOLOGY (2011) 737 737
JOENSUU H. RISK STRATIFICATION OF PATIENTS DIAGNOSED WITH GASTROINTESTINAL STROMAL TUMOR (2008) 723 723
Knowledge Base 6: KB 6: unlabeled (n = 469, density =21.68)
GERLACH B. CORDIER S.M. SCHMUKLE A.C. EMMERICH C.H. RIESER E. HAAS T.L. LINEAR UBIQUITINATION PREVENTS INFLAMMATION AND REGULATES IMMUNE SIGNALLING… 1422 1744
HAAS T.L. EMMERICH C.H. GERLACH B. SCHMUKLE A.C. CORDIER S.M. RIESER E. RECRUITMENT OF THE LINEAR UBIQUITIN CHAIN ASSEMBLY COMPLEX STABILIZES THE T… 1111 1344
BOISSON B. LAPLANTINE E. DOBBS K. COBAT A. TARANTINO N. HAZEN M. HUMAN HOIP AND LUBAC DEFICIENCY UNDERLIES AUTOINFLAMMATION IMMUNODEFICIENCY AMYLOP… 931 1072
DRABER P. KUPKA S. REICHERT M. DRABEROVA H. LAFONT E. DE MIGUEL D. LUBAC-RECRUITED CYLD AND A20 REGULATE GENE ACTIVATION AND CELL DEATH BY EXERTING… 890 1062
BOISSON B. LAPLANTINE E. PRANDO C. GILIANI S. ISRAELSSON E. XU Z. IMMUNODEFICIENCY AUTOINFLAMMATION AND AMYLOPECTINOSIS IN HUMANS WITH INHERITED HO… 880 998
DAMGAARD R.B. WALKER J.A. MARCO-CASANOVA P. MORGAN N.V. TITHERADGE H.L. ELLIOTT P.R. THE DEUBIQUITINASE OTULIN IS AN ESSENTIAL NEGATIVE REGULATOR O… 839 994
ELLIOTT P.R. LESKE D. HRDINKA M. BAGOLA K. FIIL B.K. MCLAUGHLIN S.H. SPATA2 LINKS CYLD TO LUBAC ACTIVATES CYLD AND CONTROLS LUBAC SIGNALING (2016) 766 910
KEUSEKOTTEN K. ELLIOTT P.R. GLOCKNER L. FIIL B.K. DAMGAARD R.B. KULATHU Y. OTULIN ANTAGONIZES LUBAC SIGNALING BY SPECIFICALLY HYDROLYZING MET1-LINK… 691 971
FIIL B.K. DAMGAARD R.B. WAGNER S.A. KEUSEKOTTEN K. FRITSCH M. BEKKER-JENSEN S. OTULIN RESTRICTS MET1-LINKED UBIQUITINATION TO CONTROL INNATE IMMUNE… 686 811
KUPKA S. DE MIGUEL D. DRABER P. MARTINO L. SURINOVA S. RITTINGER K. SPATA2-MEDIATED BINDING OF CYLD TO HOIP ENABLES CYLD RECRUITMENT TO SIGNALING C… 678 820
Knowledge Base 7: KB 7: unlabeled (n = 402, density =11.88)
CRAIG R. BEAVIS R.C. TANDEM: MATCHING PROTEINS WITH TANDEM MASS SPECTRA (2004) 1262 1262
COX J. MANN M. MAXQUANT ENABLES HIGH PEPTIDE IDENTIFICATION RATES INDIVIDUALIZED P.P.B.-RANGE MASS ACCURACIES AND PROTEOME-WIDE PROTEIN QUANTIFICAT… 699 1001
ENG J.K. MCCORMACK A.L. YATES J.R. AN APPROACH TO CORRELATE TANDEM MASS SPECTRAL DATA OF PEPTIDES WITH AMINO ACID SEQUENCES IN A PROTEIN DATABASE (… 420 439
ELIAS J.E. GYGI S.P. TARGET-DECOY SEARCH STRATEGY FOR INCREASED CONFIDENCE IN LARGE-SCALE PROTEIN IDENTIFICATIONS BY MASS SPECTROMETRY (2007) 347 374
BOWDEN P. BEAVIS R. MARSHALL J. TANDEM MASS SPECTROMETRY OF HUMAN TRYPTIC BLOOD PEPTIDES CALCULATED BY A STATISTICAL ALGORITHM AND CAPTURED BY A RE… 312 312
SCHWARTZ J.C. SENKO M.W. SYKA J.E. A TWO-DIMENSIONAL QUADRUPOLE ION TRAP MASS SPECTROMETER (2002) 304 304
BENJAMINI Y. HOCHBERG Y. CONTROLLING FALSE DISCOVERY RATE: A PRACTICAL APPROACH TO MULTIPLE TESTING (1995) 295 295
BOWDEN P. META SEQUENCE ANALYSIS OF HUMAN BLOOD PEPTIDES AND THEIR PARENT PROTEINS (2010) 294 294
BOWDEN P. QUANTITATIVE STATISTICAL ANALYSIS OF STANDARD AND HUMAN BLOOD PROTEINS FROM LIQUID CHROMATOGRAPHY ELECTROSPRAY IONIZATION AND TANDEM MASS… 294 294
FLORENTINUS A.K. IDENTIFICATION AND QUANTIFICATION OF PEPTIDES AND PROTEINS SECRETED FROM PROSTATE EPITHELIAL CELLS BY UNBIASED LIQUID CHROMATOGRAP… 294 294

Development of Knowledge Bases

Technical description

In a co-cittion network, the strength of the relationship between a reference pair \(m\) and \(n\) (\(s_{m,n}^{coc}\)) is expressed by the number of publications \(C\) which are jointly citing reference \(m\) and \(n\).

\[s_{m,n}^{coc} = \sum_i c_{i,m} c_{i,n}\]

The intuition here is that references which are frequently cited together are likely to share commonalities in theory, topic, methodology, or context. It can be interpreted as a measure of similarity as evaluated by other researchers that decide to jointly cite both references. Because the publication process is time-consuming, co-citation is a backward-looking measure, which is appropriate to map the relationship between core literature of a field.

Research Areas: Bibliographic coupling analysis

Research Areas main summary

This is arguably the more interesting part. Here, we identify the literature’s current knowledge frontier by carrying out a bibliographic coupling analysis of the publications in our corpus. This measure uses bibliographical information of publications to establish a similarity relationship between them. Again, method details to be found in the tab Technical description. As you will see, we identify the main research area, but also a set of adjacent research areas with some theoretical/methodological/application overlap.

To identify communities in the field’s knowledge frontier (labeled research areas) we again use the Lovain Algorithm (Blondel et al., 2008). We identify the following communities = research areas.

label AU PY TI dgr_int TC TC_year
Research Area 1: RA 1: unlabeled (n = 1178, density =0.14)
RA 1: unlabeled GBD 2016 PARKINSON’S D… 2018 GLOBAL, REGIONAL, AND NATIONAL BURDEN OF PARKINSON’S DISEASE, 1990–2016: A SYSTEMATIC ANALYSIS FOR THE GLOBAL BURDEN OF DI… 3.14 855 213.75
RA 1: unlabeled ARMSTRONG MJ;OKUN MS 2020 DIAGNOSIS AND TREATMENT OF PARKINSON DISEASE: A REVIEW 1.95 507 253.50
RA 1: unlabeled POSTUMA RB;IRANZO A;HU… 2019 RISK AND PREDICTORS OF DEMENTIA AND PARKINSONISM IN IDIOPATHIC REM SLEEP BEHAVIOUR DISORDER: A MULTICENTRE STUDY 2.63 313 104.33
RA 1: unlabeled OBESO JA;STAMELOU M;GO… 2017 PAST, PRESENT, AND FUTURE OF PARKINSON’S DISEASE: A SPECIAL ESSAY ON THE 200TH ANNIVERSARY OF THE SHAKING PALSY 2.26 363 72.60
RA 1: unlabeled DUNCAN GW;FIRBANK MJ;Y… 2016 GRAY AND WHITE MATTER IMAGING: A BIOMARKER FOR COGNITIVE IMPAIRMENT IN EARLY PARKINSON’S DISEASE? 6.16 98 16.33
RA 1: unlabeled TYSNES O-B;STORSTEIN A 2017 EPIDEMIOLOGY OF PARKINSON’S DISEASE 0.62 904 180.80
RA 1: unlabeled REICH SG;SAVITT JM 2019 PARKINSON’S DISEASE 3.83 137 45.67
RA 1: unlabeled LAWSON RA;YARNALL AJ;D… 2016 COGNITIVE DECLINE AND QUALITY OF LIFE IN INCIDENT PARKINSON’S DISEASE: THE ROLE OF ATTENTION 4.98 99 16.50
RA 1: unlabeled BAE YJ;KIM J-M;KIM E;L… 2016 LOSS OF NIGRAL HYPERINTENSITY ON 3 TESLA MRI OF PARKINSONISM: COMPARISON WITH 123I-FP-CIT SPECT 6.64 73 12.17
RA 1: unlabeled IRWIN DJ;GROSSMAN M;WE… 2017 NEUROPATHOLOGICAL AND GENETIC CORRELATES OF SURVIVAL AND DEMENTIA ONSET IN SYNUCLEINOPATHIES: A RETROSPECTIVE ANALYSIS 1.93 247 49.40
Research Area 2: RA 2: unlabeled (n = 1054, density =0.23)
RA 2: unlabeled SWATEK KN;KOMANDER D 2016 UBIQUITIN MODIFICATIONS 5.51 805 134.17
RA 2: unlabeled YAU R;RAPE M 2016 THE INCREASING COMPLEXITY OF THE UBIQUITIN CODE 6.64 510 85.00
RA 2: unlabeled MEVISSEN TET;KOMANDER D 2017 MECHANISMS OF DEUBIQUITINASE SPECIFICITY AND REGULATION 5.75 432 86.40
RA 2: unlabeled AKUTSU M;DIKIC I;BREMM A 2016 UBIQUITIN CHAIN DIVERSITY AT A GLANCE 8.68 257 42.83
RA 2: unlabeled KWON YT;CIECHANOVER A 2017 THE UBIQUITIN CODE IN THE UBIQUITIN-PROTEASOME SYSTEM AND AUTOPHAGY 6.68 312 62.40
RA 2: unlabeled ZHENG N;SHABEK N 2017 UBIQUITIN LIGASES: STRUCTURE, FUNCTION, AND REGULATION 3.25 515 103.00
RA 2: unlabeled BUETOW L;HUANG DT 2016 STRUCTURAL INSIGHTS INTO THE CATALYSIS AND REGULATION OF E3 UBIQUITIN LIGASES 6.44 252 42.00
RA 2: unlabeled OHTAKE F;SAEKI Y;ISHID… 2016 THE K48-K63 BRANCHED UBIQUITIN CHAIN REGULATES NF-ΚB SIGNALING 9.25 155 25.83
RA 2: unlabeled CLAGUE MJ;URBÉ S;KOMAN… 2019 BREAKING THE CHAINS: DEUBIQUITYLATING ENZYME SPECIFICITY BEGETS FUNCTION 5.44 253 84.33
RA 2: unlabeled OHTAKE F;TSUCHIYA H;SA… 2018 K63 UBIQUITYLATION TRIGGERS PROTEASOMAL DEGRADATION BY SEEDING BRANCHED UBIQUITIN CHAINS 10.00 117 29.25
Research Area 3: RA 3: unlabeled (n = 671, density =0.23)
RA 3: unlabeled SPITZER MH;NOLAN GP 2016 MASS CYTOMETRY: SINGLE CELLS, MANY FEATURES 8.42 607 101.17
RA 3: unlabeled BECHT E;MCINNES L;HEAL… 2019 DIMENSIONALITY REDUCTION FOR VISUALIZING SINGLE-CELL DATA USING UMAP 2.94 1214 404.67
RA 3: unlabeled WOLF FA;ANGERER P;THEI… 2018 SCANPY: LARGE-SCALE SINGLE-CELL GENE EXPRESSION DATA ANALYSIS 2.23 1050 262.50
RA 3: unlabeled CHEVRIER S;LEVINE JH;Z… 2017 AN IMMUNE ATLAS OF CLEAR CELL RENAL CELL CARCINOMA 3.18 485 97.00
RA 3: unlabeled WEI SC;LEVINE JH;COGDI… 2017 DISTINCT CELLULAR MECHANISMS UNDERLIE ANTI-CTLA-4 AND ANTI-PD-1 CHECKPOINT BLOCKADE 2.26 643 128.60
RA 3: unlabeled AZIZI E;CARR AJ;PLITAS… 2018 SINGLE-CELL MAP OF DIVERSE IMMUNE PHENOTYPES IN THE BREAST TUMOR MICROENVIRONMENT 1.69 653 163.25
RA 3: unlabeled SAEYS Y;VAN GASSEN S;L… 2016 COMPUTATIONAL FLOW CYTOMETRY: HELPING TO MAKE SENSE OF HIGH-DIMENSIONAL IMMUNOLOGY DATA 3.92 252 42.00
RA 3: unlabeled WAGNER J;RAPSOMANIKI M… 2019 A SINGLE-CELL ATLAS OF THE TUMOR AND IMMUNE ECOSYSTEM OF HUMAN BREAST CANCER 3.44 272 90.67
RA 3: unlabeled SETTY M;TADMOR MD;REIC… 2016 WISHBONE IDENTIFIES BIFURCATING DEVELOPMENTAL TRAJECTORIES FROM SINGLE-CELL DATA 2.91 317 52.83
RA 3: unlabeled HABER AL;BITON M;ROGEL… 2017 A SINGLE-CELL SURVEY OF THE SMALL INTESTINAL EPITHELIUM 1.39 603 120.60
Research Area 4: RA 4: unlabeled (n = 491, density =0.27)
RA 4: unlabeled SIDDIQUI I;SCHAEUBLE K… 2019 INTRATUMORAL TCF1 + PD-1 + CD8 + T CELLS WITH STEM-LIKE PROPERTIES PROMOTE TUMOR CONTROL IN RESPONSE TO VACCINATION AND CH… 1.93 435 145.00
RA 4: unlabeled CASSETTA L;FRAGKOGIANN… 2019 HUMAN TUMOR-ASSOCIATED MACROPHAGE AND MONOCYTE TRANSCRIPTIONAL LANDSCAPES REVEAL CANCER-SPECIFIC REPROGRAMMING, BIOMARKERS… 1.39 313 104.33
RA 4: unlabeled JIA D;LI S;LI D;XUE H;… 2018 MINING TCGA DATABASE FOR GENES OF PROGNOSTIC VALUE IN GLIOBLASTOMA MICROENVIRONMENT 2.06 186 46.50
RA 4: unlabeled HUNDHAUSEN C;ROTH A;WH… 2016 ENHANCED T CELL RESPONSES TO IL-6 IN TYPE 1 DIABETES ARE ASSOCIATED WITH EARLY CLINICAL DISEASE AND INCREASED IL-6 RECEPTO… 3.56 57 9.50
RA 4: unlabeled CHEN L;LU D;SUN K;XU Y… 2019 IDENTIFICATION OF BIOMARKERS ASSOCIATED WITH DIAGNOSIS AND PROGNOSIS OF COLORECTAL CANCER PATIENTS BASED ON INTEGRATED BIO… 3.41 58 19.33
RA 4: unlabeled CHAUDHARY K;POIRION OB… 2018 DEEP LEARNING–BASED MULTI-OMICS INTEGRATION ROBUSTLY PREDICTS SURVIVAL IN LIVER CANCER 0.56 347 86.75
RA 4: unlabeled MAN K;GABRIEL SS;LIAO … 2017 TRANSCRIPTION FACTOR IRF4 PROMOTES CD8+ T CELL EXHAUSTION AND LIMITS THE DEVELOPMENT OF MEMORY-LIKE T CELLS DURING CHRONIC… 1.17 166 33.20
RA 4: unlabeled OMENETTI S;BUSSI C;MET… 2019 THE INTESTINE HARBORS FUNCTIONALLY DISTINCT HOMEOSTATIC TISSUE-RESIDENT AND INFLAMMATORY TH17 CELLS 1.32 106 35.33
RA 4: unlabeled CHEN L;YUAN L;WANG Y;W… 2017 CO-EXPRESSION NETWORK ANALYSIS IDENTIFIED FCER1G IN ASSOCIATION WITH PROGRESSION AND PROGNOSIS IN HUMAN CLEAR CELL RENAL C… 1.93 71 14.20
RA 4: unlabeled MEYER-SCHALLER N;CARDN… 2019 A HIERARCHICAL REGULATORY LANDSCAPE DURING THE MULTIPLE STAGES OF EMT 3.34 36 12.00
Research Area 5: RA 5: unlabeled (n = 369, density =0.65)
RA 5: unlabeled MEHREN MV;JOENSUU H 2018 GASTROINTESTINAL STROMAL TUMORS 9.83 129 32.25
RA 5: unlabeled JOENSUU H;ERIKSSON M;S… 2016 ADJUVANT IMATINIB FOR HIGH-RISK GI STROMAL TUMOR: ANALYSIS OF A RANDOMIZED TRIAL 8.80 135 22.50
RA 5: unlabeled JOENSUU H;WARDELMANN E… 2017 EFFECT OF KIT AND PDGFRA MUTATIONS ON SURVIVAL IN PATIENTS WITH GASTROINTESTINAL STROMAL TUMORS TREATED WITH ADJUVANT IMAT… 6.98 93 18.60
RA 5: unlabeled BLAY J-Y;SERRANO C;HEI… 2020 RIPRETINIB IN PATIENTS WITH ADVANCED GASTROINTESTINAL STROMAL TUMOURS (INVICTUS): A DOUBLE-BLIND, RANDOMISED, PLACEBO-CONT… 5.20 103 51.50
RA 5: unlabeled NISHIDA T;BLAY J-Y;HIR… 2016 THE STANDARD DIAGNOSIS, TREATMENT, AND FOLLOW-UP OF GASTROINTESTINAL STROMAL TUMORS BASED ON GUIDELINES 2.56 209 34.83
RA 5: unlabeled CASALI PG;FUMAGALLI E;… 2017 TEN-YEAR PROGRESSION-FREE AND OVERALL SURVIVAL IN PATIENTS WITH UNRESECTABLE OR METASTATIC GI STROMAL TUMORS: LONG-TERM AN… 5.35 92 18.40
RA 5: unlabeled RAUT CP;ESPAT NJ;MAKI … 2018 EFFICACY AND TOLERABILITY OF 5-YEAR ADJUVANT IMATINIB TREATMENT FOR PATIENTS WITH RESECTED INTERMEDIATE- OR HIGH-RISK PRIM… 9.00 50 12.50
RA 5: unlabeled HEINRICH MC;JONES RL;V… 2020 AVAPRITINIB IN ADVANCED PDGFRA D842V-MUTANT GASTROINTESTINAL STROMAL TUMOUR (NAVIGATOR): A MULTICENTRE, OPEN-LABEL, PHASE … 4.59 90 45.00
RA 5: unlabeled KOO D-H;RYU M-H;KIM K-… 2016 ASIAN CONSENSUS GUIDELINES FOR THE DIAGNOSIS AND MANAGEMENT OF GASTROINTESTINAL STROMAL TUMOR 4.54 90 15.00
RA 5: unlabeled AKAHOSHI K;OYA M;KOGA … 2018 CURRENT CLINICAL MANAGEMENT OF GASTROINTESTINAL STROMAL TUMOR 4.01 98 24.50
Research Area 6: RA 6: unlabeled (n = 330, density =0.38)
RA 6: unlabeled PINO LK;SEARLE BC;BOLL… 2020 THE SKYLINE ECOSYSTEM: INFORMATICS FOR QUANTITATIVE MASS SPECTROMETRY PROTEOMICS 2.61 193 96.50
RA 6: unlabeled THE M;MACCOSS MJ;NOBLE… 2016 FAST AND ACCURATE PROTEIN FALSE DISCOVERY RATES ON LARGE-SCALE PROTEOMICS DATA SETS WITH PERCOLATOR 3.0 3.61 130 21.67
RA 6: unlabeled LANGELLA O;VALOT B;BAL… 2017 X!TANDEMPIPELINE: A TOOL TO MANAGE SEQUENCE REDUNDANCY FOR PROTEIN INFERENCE AND PHOSPHOSITE IDENTIFICATION 4.36 100 20.00
RA 6: unlabeled GEYER PE;HOLDT LM;TEUP… 2017 REVISITING BIOMARKER DISCOVERY BY PLASMA PROTEOMICS 1.34 318 63.60
RA 6: unlabeled GESSULAT S;SCHMIDT T;Z… 2019 PROSIT: PROTEOME-WIDE PREDICTION OF PEPTIDE TANDEM MASS SPECTRA BY DEEP LEARNING 1.11 224 74.67
RA 6: unlabeled GEYER PE;WEWER ALBRECH… 2016 PROTEOMICS REVEALS THE EFFECTS OF SUSTAINED WEIGHT LOSS ON THE HUMAN PLASMA PROTEOME 1.59 118 19.67
RA 6: unlabeled RANDLES MJ;HUMPHRIES M… 2017 PROTEOMIC DEFINITIONS OF BASEMENT MEMBRANE COMPOSITION IN HEALTH AND DISEASE 2.00 76 15.20
RA 6: unlabeled WICHMANN C;MEIER F;WIN… 2019 MAXQUANT.LIVE ENABLES GLOBAL TARGETING OF MORE THAN 25,000 PEPTIDES 3.09 47 15.67
RA 6: unlabeled TSOU C-C;TSAI C-F;TEO … 2016 UNTARGETED, SPECTRAL LIBRARY-FREE ANALYSIS OF DATA-INDEPENDENT ACQUISITION PROTEOMICS DATA GENERATED USING ORBITRAP MASS S… 2.90 46 7.67
RA 6: unlabeled SCHWENK JM;OMENN GS;SU… 2017 THE HUMAN PLASMA PROTEOME DRAFT OF 2017: BUILDING ON THE HUMAN PLASMA PEPTIDEATLAS FROM MASS SPECTROMETRY AND COMPLEMENTAR… 1.21 109 21.80
Research Area 7: RA 7: unlabeled (n = 299, density =3.15)
RA 7: unlabeled HESTHAVEN JS;UBBIALI S 2018 NON-INTRUSIVE REDUCED ORDER MODELING OF NONLINEAR PROBLEMS USING NEURAL NETWORKS 7.80 154 38.50
RA 7: unlabeled LIEVENS A;JACCHIA S;KA… 2016 MEASURING DIGITAL PCR QUALITY: PERFORMANCE PARAMETERS AND THEIR OPTIMIZATION 10.90 74 12.33
RA 7: unlabeled BANGALORE P;LETZGUS S;… 2017 AN ARTIFICIAL NEURAL NETWORK-BASED CONDITION MONITORING METHOD FOR WIND TURBINES, WITH APPLICATION TO THE MONITORING OF TH… 9.54 79 15.80
RA 7: unlabeled ASKHAM T;KUTZ JN 2018 VARIABLE PROJECTION METHODS FOR AN OPTIMIZED DYNAMIC MODE DECOMPOSITION 8.06 73 18.25
RA 7: unlabeled LAZZARI F;BUFFI A;NEPA… 2017 NUMERICAL INVESTIGATION OF AN UWB LOCALIZATION TECHNIQUE FOR UNMANNED AERIAL VEHICLES IN OUTDOOR SCENARIOS 11.58 41 8.20
RA 7: unlabeled LASSENBERGER A;GRÜNEWA… 2017 MONODISPERSE IRON OXIDE NANOPARTICLES BY THERMAL DECOMPOSITION: ELUCIDATING PARTICLE FORMATION BY SECOND-RESOLVED IN SITU … 7.75 60 12.00
RA 7: unlabeled BARBIERI S;DONATI OF;F… 2016 IMPACT OF THE CALCULATION ALGORITHM ON BIEXPONENTIAL FITTING OF DIFFUSION-WEIGHTED MRI IN UPPER ABDOMINAL ORGANS 7.82 58 9.67
RA 7: unlabeled ROBERT DJ;RAJEEV P;KOD… 2016 EQUATION TO PREDICT MAXIMUM PIPE STRESS INCORPORATING INTERNAL AND EXTERNAL LOADINGS ON BURIED PIPES 13.27 28 4.67
RA 7: unlabeled AMIGO JM;DEL OLMO ALVA… 2016 STALING OF WHITE WHEAT BREAD CRUMB AND EFFECT OF MALTOGENIC Α-AMYLASES. PART 1: SPATIAL DISTRIBUTION AND KINETIC MODELING … 9.76 38 6.33
RA 7: unlabeled ARSLAN D;CHONG KE;MIRO… 2017 ANGLE-SELECTIVE ALL-DIELECTRIC HUYGENS’ METASURFACES 9.43 39 7.80

Development

Connectivity between the research areas

Technical description

In a bibliographic coupling network, the coupling-strength between publications is determined by the number of commonly cited references they share, assuming a common pool of references to indicate similarity in context, methods, or theory. Formally, the strength of the relationship between a publication pair \(i\) and \(j\) (\(s_{i,j}^{bib}\)) is expressed by the number of commonly cited references.

\[s_{i,j}^{bib} = \sum_m c_{i,m} c_{j,m}\]

Since our corpus contains publications which differ strongly in terms of the number of cited references, we normalize the coupling strength by the Jaccard similarity coefficient. Here, we weight the intercept of two publications’ bibliography (shared refeences) by their union (number of all references cited by either \(i\) or \(j\)). It is bounded between zero and one, where one indicates the two publications to have an identical bibliography, and zero that they do not share any cited reference. Thereby, we prevent publications from having high coupling strength due to a large bibliography (e.g., literature surveys).

\[S_{i,j}^{jac-bib} =\frac{C(i \cap j)}{C(i \cup j)} = \frac{s_{i,j}^{bib}}{c_i + c_j - s_{i,j}^{bib}}\]

More recent articles have a higher pool of possible references to co-cite to, hence they are more likely to be coupled. Consequently, bibliographic coupling represents a forward looking measure, and the method of choice to identify the current knowledge frontier at the point of analysis.

Knowledge Bases, Research Areas & Topics Interaction

Endnotes

All results are preliminary so far…